In this paper, we take a preliminary step towards solving the problem of causal discovery in knowledge tracing, i.e., finding the underlying causal relationship among different skills from real-world student response data. This problem is important since it can potentially help us understand the causal relationship between different skills without extensive A/B testing, which can potentially help educators to design better curricula according to skill prerequisite information. Specifically, we propose a conceptual solution, a novel causal gated recurrent unit (GRU) module in a modified deep knowledge tracing model, which uses i) a learnable permutation matrix for causal ordering among skills and ii) an optionally learnable lower-triangular matrix for causal structure among skills. We also detail how to learn the model parameters in an end-to-end, differentiable way. Our solution placed among the top entries in Task 3 of the NeurIPS 2022 Challenge on Causal Insights for Learning Paths in Education. We detail preliminary experiments as evaluated on the challenge's public leaderboard since the ground truth causal structure has not been publicly released, making detailed local evaluation impossible.

翻译：本文初步探索了知识追踪中的因果发现问题，即从真实学生答题数据中挖掘不同技能间的潜在因果关系。该问题的研究价值在于，它有助于我们无需进行大规模A/B测试即可理解技能间的因果关联，从而帮助教育者根据技能先决关系设计更优课程体系。具体而言，我们提出了一种概念性解决方案：在改进的深度知识追踪模型中嵌入新型因果门控循环单元模块。该模块采用：(i) 用于技能因果排序的可学习置换矩阵；(ii) 用于技能间因果结构的可选可学习下三角矩阵。我们还详细阐述了如何以端到端可微分方式学习模型参数。本方案在NeurIPS 2022“教育路径因果洞察”挑战赛第三赛道中位列前茅。由于因果结构真实标签尚未公开，我们基于挑战赛公开排行榜进行了初步实验评估，未进行详细的本地化评估。